Unlike a device continuously used by a specific person, as in the case of a mobile terminal such as a smart phone, services adopting TVs as a medium, such as a cable TV, an Internet Protocol TV (IPTV), and satellite broadcasting, are configured such that various members constituting a household use a single device. In this situation, individual members may occasionally gather and view the same content in the same time slot, but they generally have different content preferences, and thus view different types of content in different time slots.
A typical personalization/recommendation algorithm, such as association rule mining, analyzes the usage form or pattern of each individual and provides content suitable for the individual's preferences. However, when multiple users use the same service in common using a single device as a medium, as in the case of a TV, there arises first the need to specify each individual. For this, in general, methods for receiving the profiles of individual users who intend to currently use a TV via a separate user interface have been attempted. However, the methods are inconvenient from the standpoint of usability, and in reality it is difficult to induce users to designate their profiles every time they use a TV.
Further, in the case where preference information is generated using viewing information generated in a household and content is recommended depending on the preference information, a problem may arise in that content that is not of interest to the household members who actually use the corresponding TV is recommended. For example, when multiple users view a single device such as a TV, viewing patterns may vary in respective time slots every day. That is, patterns may appear in which, in the morning, beauty- or drama-related programs chiefly viewed by housewives are mainly viewed, in the afternoon, programs for children are mainly viewed, and in the evening and night time, sports programs or movies are viewed. When such patterns are integrated into a usage history for a single device and association rule mining or the like is performed, a problem may occur wherein adult programs are recommended to users who view programs for children, such as animations.
Further, such technology may be used to recommend sound source data to sound source users. However, considering the reality in which a large number of pieces of music are released every day, it is difficult for each sound source user to search for sound sources suitable for his or her preference when using sound source data as his or her ringtone, ringback tone or the like, or downloading the sound source data to his or her MP3 player or the like.
To solve this problem, there has been proposed a type of service that classifies pieces of music via the analysis of audio data, and then allows sound source users to more easily select music suitable for their preferences. However, most existing music recommendation technologies adopt techniques for selecting and analyzing only a partial section of sound source data due to the problem of low efficiency arising when an entire musical composition is analyzed. Further, since existing music recommendation technologies merely recommend sound sources based only on pieces of music selected or listened to by a sound source user, there may frequently occur the case where sound sources unsuitable for the sound source user's preference are recommended.
As related preceding technologies, there are Korean Patent Application Publication No. 10-2011-0071715 (Date of publication: Jun. 29, 2011) (entitled “System of IPTV service for providing community service”) and Korean Patent Application Publication No. 10-2008-000234 (Date of publication: Jan. 4, 2008) (entitled “System and method for recommending music”).